A sustainable IoHT based computationally intelligent healthcare monitoring system for lung cancer risk detection. (September 2021)

• Record Type: Journal Article
• Authors: Mishra, Sushruta; Thakkar, Hiren Kumar; Mallick, Pradeep Kumar; Tiwari, Prayag; Alamri, Atif
• Is Part Of: Sustainable cities and society. Volume 72(2021)
• Publication Date: 2021-09
• Subjects: Sustainable healthcare -- Internet of Health Things (IoHT) -- Heuristics -- Random forest -- Greedy Best First Search (GBFS) -- Lung cancer -- Classification -- Computational intelligence; Sustainable urban development -- Periodicals; Sustainable buildings -- Periodicals; Urban ecology (Sociology) -- Periodicals; 307.76
• DOI: 10.1016/j.scs.2021.103079
• Languages: English
• ISSNs: 2210-6707

Abstract

Highlights: A sustainable IoHT model is discussed for lung cancer diagnosis in smart cities. IoHT unit stores lung cancer data of patients in cloud storage through its sensors. The predictive unit detects and classifies lung cancer patients based on symptoms. Greedy Best First Search is a feature selector, and the random forest is the classifier. The proposed model can assist medical experts in cancer diagnosis with limited resources. Abstract: A sustainable healthcare focuses on enhancing and restoring public health parameters thereby reducing gloomy impacts on social, economic and environmental elements of a sustainable city. Though it has uplifted public health, yet the rise of chronic diseases is a concern in sustainable cities. In this work, a sustainable lung cancer detection model is developed to integrate the Internet of Health Things (IoHT) and computational intelligence, causing the least harm to the environment. IoHT unit retains connectivity continuously generates data from patients. Heuristic Greedy Best First Search (GBFS) algorithm is used to select most relevant attributes of lung cancer data upon which random forest algorithm is applied to classify and differentiates lung cancer affected patients from normal ones based on detected symptoms. It is observed during the experiment that the GBFS-Random forest model shows a promising outcome. While an optimal accuracy of 98.8 % was generated, simultaneously, the least latency of 1.16 s was noted. Specificity and sensitivity recorded with the proposed model on lung cancer data are 97.5 % and 97.8 %, respectively. The mean accuracy, specificity, sensitivity, and f-score value recorded is 96.96 %, 96.26 %, 96.34 %, and 96.32 %, respectively, over various types of cancer datasets implemented. The developed smart and intelligent model is sustainable. It reduces unnecessary manual overheads, safe, preserves resources and human resources, and assists medical professionals in quick and reliable decision making on lung cancer diagnosis.